ArfGAP with GTPase domain, ankyrin repeat and PH domain 2Genealiases: CENTG1 · GGAP2 · PIKE
Q-omics provides the consensus-scored AGAP2 profile across patient tissues and cancer cell-line models. AGAP2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, AGAP2 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, AGAP2 protein abundance shows 25,948 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, KIRC, and LSCC as cancer lineages where AGAP2 shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for AGAP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AGAP2 survival associations across molecular data types. AGAP2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (8) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AGAP2 RNA expression–survival associations across cancer types. High AGAP2 expression shows unfavorable associations in ACC and UVM, but favorable associations in HNSC, UCEC, SKCM and LUAD. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify ACC as the clearest survival context for AGAP2 RNA expression.
This table summarizes AGAP2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for AGAP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AGAP2 shows higher tumor expression in KIRC, HNSC, LIHC, KIRP, BLCA and STAD. The KIRC box plot shows higher AGAP2 RNA expression in tumor versus normal tissue (log2 FC = +1.424, t-test p < 0.001).
This table shows molecular features associated with AGAP2 in patient tissues and cancer cell lines. In patient samples, AGAP2 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, AGAP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
